Fulcrum
Fulcrum is consolidating on Esri, with Google Maps gone September 1
A side-by-side editorial comparison of Deequ and Mode Analytics — release velocity, themes, recent moves, and the top alternatives to consider.
| Feature | Deequ | Mode Analytics |
|---|---|---|
| Sector | Analytics | Analytics |
| Velocity score | 0.0 | 2.5 |
| Sparks · 30d | 0 | 0 |
| Top themes | data-quality, spark, dqdl, jvm-library | business intelligence, spreadsheet ui, cross-source joins, sql editor |
| Last editorial update | 15h ago | 3mo ago |
| Website | Visit → | — |
Deequ ships GitHub tags whose release notes are one commit message long
Deequ is a data quality library for Spark, and it releases per Spark version — tags carry a -spark-3.5 or -spark-4.0 suffix, so the same code line ships more than once. The release notes are whatever the last commit message said, which here means each entry is a single line. Four releases landed in a month between March and April 2026, and only two of them contain any product change: a Range analyzer with DQDL rule support, and a processRowsTyped API for typed outcome access.
Mode is converging spreadsheets, SQL, Python, and cross-source joins into one analyst surface.
Mode is making its core report editor more flexible and analyst-friendly: a native Excel-style spreadsheet mode with 70+ formulas alongside SQL and Python, a Data Mashup capability for cross-warehouse joins without ETL, a substantially overhauled SQL editor, shareable filtered URLs, and granular per-viz downloads in white-label embeds. Admin-side governance has kept pace with admin-managed refresh schedules and automated data retention policies.
Deequ is a data quality library for Spark, and it releases per Spark version — tags carry a -spark-3.5 or -spark-4.0 suffix, so the same code line ships more than once. The release notes are whatever the last commit message said, which here means each entry is a single line. Four releases landed in a month between March and April 2026, and only two of them contain any product change: a Range analyzer with DQDL rule support, and a processRowsTyped API for typed outcome access.
The visible work points in one direction — making check results programmatically consumable rather than just readable. A typed outcome API and a rule language binding are what you build when Deequ is being called from a pipeline that reacts to the result, not from a notebook where a human reads it. The column-pruning override added alongside the Range analyzer suggests the same attention on the cost side, keeping analyzers from scanning columns they do not reference.
The entries are too thin to support a confident read of what comes next; the only clear pattern is that each change will ship separately against Spark 3.5 and Spark 4.0, so the version skew between those branches will keep widening.
Mode is making its core report editor more flexible and analyst-friendly: a native Excel-style spreadsheet mode with 70+ formulas alongside SQL and Python, a Data Mashup capability for cross-warehouse joins without ETL, a substantially overhauled SQL editor, shareable filtered URLs, and granular per-viz downloads in white-label embeds. Admin-side governance has kept pace with admin-managed refresh schedules and automated data retention policies.
Mode is doubling down on the 'one workspace for SQL, Python, and spreadsheets' positioning at a moment when most BI tools are picking a lane. The cross-source Data Mashup is the more strategic bet — it positions Mode as a thin governance/analysis layer sitting above multiple warehouses, useful in shops with fragmented data infrastructure. White-label embedding work hints at continued investment in the analytics-for-customers segment.
Expect AI/copilot features to layer onto the new SQL editor and spreadsheet surfaces (natural-language query, formula suggestion), and Data Mashup to graduate from invite-only to GA with notebook-output and CSV/Excel sources following. White-label embeds are a likely target for richer customer-facing interactivity given Mode's product-analytics-embed customer base.
Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either Deequ or Mode Analytics.
Fulcrum is consolidating on Esri, with Google Maps gone September 1
Omni ships weekly, and almost every week the headline item is an AI feature
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Baremaps got geoparquet and hillshading, then went quiet for eighteen months in incubation
Marquez spent 2024 turning a lineage store into a UI, then stopped releasing
Amundsen's last release was a config flag, and the feed has been silent for two years
See all Deequ alternatives → · See all Mode Analytics alternatives →
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
They serve adjacent needs but don't currently overlap on shipped themes. Mode Analytics is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. Mode Analytics is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.
Top Deequ alternatives in Analytics are ranked by recent ship velocity. Browse the "Deequ alternatives" section above for the current picks, or visit /alternatives/deequ for the full list with editorial commentary on each.
Top Mode Analytics alternatives in Analytics are ranked by recent ship velocity. Browse the "Mode Analytics alternatives" section above for the current picks, or visit /alternatives/mode for the full list with editorial commentary on each.